{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confounding Example: Finding causal effects from observed data\n",
    "\n",
    "Suppose you are given some data with treatment and outcome. Can you determine whether the treatment causes the outcome, or the correlation is purely due to another common cause?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.append(os.path.abspath(\"../../../\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets, dowhy.plotter\n",
    "import logging\n",
    "logging.getLogger(\"dowhy\").setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's create a mystery dataset for which we need to determine whether there is a causal effect.\n",
    "\n",
    "Creating the dataset. It is generated from either one of two models:\n",
    "* **Model 1**: Treatment does cause outcome. \n",
    "* **Model 2**: Treatment does not cause outcome. All observed correlation is due to a common cause."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Treatment    Outcome        w0\n",
      "0   4.424284   8.937867 -1.420934\n",
      "1  10.131230  20.200164  3.987792\n",
      "2   2.728258   5.274174 -3.358227\n",
      "3   2.550623   5.114946 -3.416812\n",
      "4   4.286140   8.704681 -1.410838\n"
     ]
    }
   ],
   "source": [
    "rvar = 1 if np.random.uniform() >0.5 else 0\n",
    "data_dict = dowhy.datasets.xy_dataset(10000, effect=rvar, \n",
    "                                      num_common_causes=1, \n",
    "                                      sd_error=0.2)                                               \n",
    "df = data_dict['df']                                                                                 \n",
    "print(df[[\"Treatment\", \"Outcome\", \"w0\"]].head())\n",
    "                                                                                                                                                                                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dowhy.plotter.plot_treatment_outcome(df[data_dict[\"treatment_name\"]], df[data_dict[\"outcome_name\"]],\n",
    "                             df[data_dict[\"time_val\"]])                                                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using DoWhy to resolve the mystery: *Does Treatment cause Outcome?*\n",
    "### STEP 1: Model the problem as a causal graph\n",
    "Initializing the causal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n"
     ]
    }
   ],
   "source": [
    "model= CausalModel(                                                                                                                      \n",
    "        data=df,                                                                                                                         \n",
    "        treatment=data_dict[\"treatment_name\"],                                                                                           \n",
    "        outcome=data_dict[\"outcome_name\"],                                                                                               \n",
    "        common_causes=data_dict[\"common_causes_names\"],                                                                                  \n",
    "        instruments=data_dict[\"instrument_names\"])                                                                                       \n",
    "model.view_model(layout=\"dot\")                                                                                                                                                                                                                                             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the causal model stored in the local file \"causal_model.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 2: Identify causal effect using properties of the formal causal graph\n",
    "Identify the causal effect using properties of the causal graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARN: Do you want to continue by ignoring any unobserved confounders? (use proceed_when_unidentifiable=True to disable this prompt) [y/n] y\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor1 (Default)\n",
      "Estimand expression:\n",
      "     d                               \n",
      "────────────(Expectation(Outcome|w0))\n",
      "d[Treatment]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,U) = P(Outcome|Treatment,w0)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable found!\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = model.identify_effect()\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 3: Estimate the causal effect\n",
    "\n",
    "Once we have identified the estimand, we can use any statistical method to estimate the causal effect. \n",
    "\n",
    "Let's use Linear Regression for simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Causal Estimate is 0.9905610344994829\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimate = model.estimate_effect(identified_estimand,\n",
    "        method_name=\"backdoor.linear_regression\")\n",
    "print(\"Causal Estimate is \" + str(estimate.value))\n",
    "\n",
    "# Plot Slope of line between treamtent and outcome =causal effect                                                                                                 \n",
    "dowhy.plotter.plot_causal_effect(estimate, df[data_dict[\"treatment_name\"]], df[data_dict[\"outcome_name\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking if the estimate is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoWhy estimate is 0.9905610344994829\n",
      "Actual true causal effect was 1\n"
     ]
    }
   ],
   "source": [
    "print(\"DoWhy estimate is \" + str(estimate.value)) \n",
    "print (\"Actual true causal effect was {0}\".format(rvar))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Refuting the estimate\n",
    "\n",
    "We can also refute the estimate to check its robustness to assumptions (*aka* sensitivity analysis, but on steroids). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a random common cause variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a Random Common Cause\n",
      "Estimated effect:0.9905610344994829\n",
      "New effect:0.9904200614048078\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_random=model.refute_estimate(identified_estimand, estimate, method_name=\"random_common_cause\")\n",
    "print(res_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing treatment with a random (placebo) variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a Placebo Treatment\n",
      "Estimated effect:0.9905610344994829\n",
      "New effect:3.0487607018869765e-06\n",
      "p value:0.45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_placebo=model.refute_estimate(identified_estimand, estimate,\n",
    "        method_name=\"placebo_treatment_refuter\", placebo_type=\"permute\")\n",
    "print(res_placebo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing a random subset of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a subset of data\n",
      "Estimated effect:0.9905610344994829\n",
      "New effect:0.990524809173428\n",
      "p value:0.48\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_subset=model.refute_estimate(identified_estimand, estimate,\n",
    "        method_name=\"data_subset_refuter\", subset_fraction=0.9)\n",
    "print(res_subset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our causal estimator is robust to simple refutations."
   ]
  }
 ],
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